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Automatic And Perceptual Discrimination Between Dysarthria, Apraxia of Speech, and Neurotypical Speech

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TLDR
In this article, a three-class automatic technique and a set of handcrafted features for the discrimination of dysarthria, apraxia of speech (AoS), and neurotypical speech was proposed.
Abstract
Automatic techniques in the context of motor speech disorders (MSDs) are typically two-class techniques aiming to discriminate between dysarthria and neurotypical speech or between dysarthria and apraxia of speech (AoS). Further, although such techniques are proposed to support the perceptual assessment of clinicians, the automatic and perceptual classification accuracy has never been compared. In this paper, we investigate a three-class automatic technique and a set of handcrafted features for the discrimination of dysarthria, AoS and neurotypical speech. Instead of following the commonly used One-versus-One or One-versus-Rest approaches for multi-class classification, a hierarchical approach is proposed. Further, a perceptual study is conducted where speech and language pathologists are asked to listen to recordings of dysarthria, AoS, and neurotypical speech and decide which class the recordings belong to. The proposed automatic technique is evaluated on the same recordings and the automatic and perceptual classification performance are compared. The presented results show that the hierarchical classification approach yields a higher classification accuracy than baseline One-versus-One and One-versus-Rest approaches. Further, the presented results show that the automatic approach yields a higher classification accuracy than the perceptual assessment of speech and language pathologists, demonstrating the potential advantages of integrating automatic tools in clinical practice.

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Citations
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Journal ArticleDOI

Perceptual Classification of Motor Speech Disorders: The Role of Severity, Speech Task, and Listener's Expertise

TL;DR: In this paper , the accuracy of speech-language pathologists in perceptually classifying apraxia of speech (AoS) and dysarthria is impacted by speech task, severity of MSD, and listener's expertise and which perceptual features they use to classify.
Journal ArticleDOI

Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s

TL;DR: In this article , the authors used decision trees for two-class automatic classification of different pairs of MSD subtypes based on seven deviance scores computed in MonPaGe-2.0.s against matched normative data.
Proceedings ArticleDOI

A Survey on ASR Systems for Dysarthric Speech

TL;DR: In this paper , the authors present a brief understanding of dysarthric speech characteristics and behavior and present several attempts that have been made to make robust ASR systems for dysarthrous speech.
Journal ArticleDOI

Hierarchical Multi-Class Classification of Voice Disorders Using Self-Supervised Models and Glottal Features

TL;DR: In this paper , a hierarchical classifier was used to detect laryngeal voice disorders, and the best performance was achieved by using features from wav2vec 2.0 LARGE together with hierarchical classification.
References
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Proceedings ArticleDOI

Recent developments in openSMILE, the munich open-source multimedia feature extractor

TL;DR: OpenSMILE 2.0 as mentioned in this paper unifies feature extraction paradigms from speech, music, and general sound events with basic video features for multi-modal processing, allowing for time synchronization of parameters, on-line incremental processing as well as off-line and batch processing, and the extraction of statistical functionals (feature summaries).
Journal ArticleDOI

Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease

TL;DR: It is found that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects, and are seen as an important step toward noninvasive diagnostic decision support in PD.
Journal ArticleDOI

Formant centralization ratio: a proposal for a new acoustic measure of dysarthric speech.

TL;DR: The present findings indicate that the FCR is a sensitive, valid, and reliable acoustic metric for distinguishing dysarthric from unimpaired speech and for monitoring treatment effects, probably because of reduced sensitivity to interspeaker variability and enhanced sensitivity to vowel centralization.
Journal ArticleDOI

Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis

TL;DR: This paper introduces a hierarchical technique to recursively decompose a C-class problem into C_1 two-(meta) class problems, and introduces a generalised modular learning framework used to partition a set of classes into two disjoint groups called meta-classes.
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